A transdisciplinary review of deep learning research and its relevance for water resources scientists C Shen Water Resources Research 54 (11), 8558-8593, 2018 | 735 | 2018 |
An overview of current applications, challenges, and future trends in distributed process-based models in hydrology S Fatichi, ER Vivoni, FL Ogden, VY Ivanov, B Mirus, D Gochis, ... Journal of Hydrology 537, 45-60, 2016 | 509 | 2016 |
Improving the representation of hydrologic processes in Earth System Models MP Clark, Y Fan, DM Lawrence, JC Adam, D Bolster, DJ Gochis, ... Water Resources Research 51 (8), 5929-5956, 2015 | 440 | 2015 |
Hillslope hydrology in global change research and earth system modeling Y Fan, M Clark, DM Lawrence, S Swenson, LE Band, SL Brantley, ... Water Resources Research 55 (2), 1737-1772, 2019 | 352 | 2019 |
An investigation of the effect of pore scale flow on average geochemical reaction rates using direct numerical simulation S Molins, D Trebotich, CI Steefel, C Shen Water Resources Research 48 (3), 2012 | 330 | 2012 |
Surface‐subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks RM Maxwell, M Putti, S Meyerhoff, JO Delfs, IM Ferguson, V Ivanov, J Kim, ... Water resources research 50 (2), 1531-1549, 2014 | 297 | 2014 |
Prolongation of SMAP to spatiotemporally seamless coverage of continental US using a deep learning neural network K Fang, C Shen, D Kifer, X Yang Geophysical Research Letters 44 (21), 11,030-11,039, 2017 | 241 | 2017 |
Enhancing streamflow forecast and extracting insights using long‐short term memory networks with data integration at continental scales D Feng, K Fang, C Shen Water Resources Research 56 (9), e2019WR026793, 2020 | 240 | 2020 |
HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community C Shen, E Laloy, A Elshorbagy, A Albert, J Bales, FJ Chang, S Ganguly, ... Hydrology and Earth System Sciences 22 (11), 5639-5656, 2018 | 221 | 2018 |
A process-based, distributed hydrologic model based on a large-scale method for surface–subsurface coupling C Shen, MS Phanikumar Advances in Water Resources 33 (12), 1524-1541, 2010 | 214 | 2010 |
Pore-scale controls on calcite dissolution rates from flow-through laboratory and numerical experiments S Molins, D Trebotich, L Yang, JB Ajo-Franklin, TJ Ligocki, C Shen, ... Environmental science & technology 48 (13), 7453-7460, 2014 | 190 | 2014 |
From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale? W Zhi, D Feng, WP Tsai, G Sterle, A Harpold, C Shen, L Li Environmental science & technology 55 (4), 2357-2368, 2021 | 154 | 2021 |
From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling WP Tsai, D Feng, M Pan, H Beck, K Lawson, Y Yang, J Liu, C Shen Nature communications 12 (1), 5988, 2021 | 119 | 2021 |
Evaluating controls on coupled hydrologic and vegetation dynamics in a humid continental climate watershed using a subsurface‐land surface processes model C Shen, J Niu, MS Phanikumar Water Resources Research 49 (5), 2552-2572, 2013 | 119 | 2013 |
The value of SMAP for long-term soil moisture estimation with the help of deep learning K Fang, M Pan, C Shen IEEE Transactions on Geoscience and Remote Sensing 57 (4), 2221-2233, 2018 | 105 | 2018 |
Near-real-time forecast of satellite-based soil moisture using long short-term memory with an adaptive data integration kernel K Fang, C Shen Journal of Hydrometeorology 21 (3), 399-413, 2020 | 101 | 2020 |
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data F Rahmani, K Lawson, W Ouyang, A Appling, S Oliver, C Shen Environmental Research Letters 16 (2), 024025, 2021 | 91 | 2021 |
Adaptive mesh refinement based on high order finite difference WENO scheme for multi-scale simulations C Shen, JM Qiu, A Christlieb Journal of Computational Physics 230 (10), 3780-3802, 2011 | 85 | 2011 |
Transferring hydrologic data across continents–leveraging data‐rich regions to improve hydrologic prediction in data‐sparse regions K Ma, D Feng, K Lawson, WP Tsai, C Liang, X Huang, A Sharma, C Shen Water Resources Research 57 (5), e2020WR028600, 2021 | 82 | 2021 |
Evaluating the potential and challenges of an uncertainty quantification method for long short‐term memory models for soil moisture predictions K Fang, D Kifer, K Lawson, C Shen Water Resources Research 56 (12), e2020WR028095, 2020 | 81* | 2020 |